Harriet J. Rogers

Fractional intracellular volume–derived Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumor analysis of multiparametric MRI findings resulted in better classification of clinic... Background In men suspected of having prostate cancer (PCa), up to 50% of men with positive multiparametric MRI (mpMRI) findings (Prostate Imaging Reporting and Data System [PI-RADS] or Likert scor...

Objective: To develop a phantom system which can be integrated with an automated injection system, eliminating the experimental variability that arises with manual injection; for the purposes of pulse sequence testing and metric derivation in hyperpolarised 13C-MR. Methods: The custom dynamic phantom was machined from Ultem and filled with a nicotinamide adenine dinucleotide and lactate dehydrogenase mixture dissolved in phosphate buffered saline. Hyperpolarised [1-13C]-pyruvate was then injected into the phantom (n = 8) via an automated syringe pump and the conversion of pyruvate to lactate monitored through a 13C imaging sequence. Results: The phantom showed low coefficient of variation for the lactate to pyruvate peak signal heights (11.6%) and dynamic area-under curve ratios (11.0%). The variance for the lactate dehydrogenase enzyme rate constant (kP) was also seen to be low at 15.6%. Conclusion: The dynamic phantom demonstrates high reproducibility for quantification of 13C-hyperpolarised MR-derived metrics. Establishing such a phantom is needed to facilitate development of hyperpolarsed 13C-MR pulse sequenced; and moreover, to enable multisite hyperpolarised 13C-MR clinical trials where assessment of metric variability across sites is critical. Advances in knowledge: The dynamic phantom developed during the course of this study will be a useful tool in testing new pulse sequences and standardisation in future hyperpolarised work.

Objective: ADC (Apparent Diffusion Coefficient) derived from Diffusion-Weighted Imaging (DWI) has shown promise as a non-invasive quantitative imaging biomarker in Wilms’ tumours. However, many non-Gaussian models could be applied to DWI. This study aimed to compare the suitability of four diffusion models (mono exponential, IVIM [Intravoxel Incoherent Motion], stretched exponential, and kurtosis) in Wilms’ tumours and the unaffected contralateral kidneys. Materials and methods: DWI data were retrospectively reviewed (110 Wilms’ tumours and 75 normal kidney datasets). The goodness of fit for each model was measured voxel-wise using Akaike Information Criteria (AIC). Mean AIC was calculated for each tumour volume (or contralateral normal kidney tissue). One-way ANOVAs with Greenhouse–Geisser correction and post hoc tests using the Bonferroni correction evaluated significant differences between AIC values; the lowest AIC indicating the optimum model. Results: IVIM and stretched exponential provided the best fits to the Wilms’ tumour DWI data. IVIM provided the best fit for the normal kidney data. Mono exponential was the least appropriate fitting method for both Wilms’ tumour and normal kidney data. Discussion: The diffusion weighted signal in Wilms’ tumours and normal kidney tissue does not exhibit a mono-exponential decay and is better described by non-Gaussian models of diffusion.

Objectives Volume of necrosis in Wilms tumour is informative of chemotherapy response. Contrast-enhanced T₁-weighted MRI (T₁w) provides a measure of necrosis using gadolinium. This study aimed to develop a non-invasive method of identifying non-enhancing (necrotic) tissue in Wilms tumour. Methods In this single centre, retrospective study, post-chemotherapy MRI data from 34 Wilms tumour patients were reviewed (March 2012–March 2017). Cases with multiple b value diffusion-weighted imaging (DWI) and T₁w imaging pre- and post-gadolinium were included. Fractional T₁ enhancement maps were generated from the gadolinium T₁w data. Multiple linear regression determined whether fitted parameters from a mono-exponential model (ADC) and bi-exponential model (IVIM – intravoxel incoherent motion) (D, D*, f) could predict fractional T₁ enhancement in Wilms tumours, using normalised pre-gadolinium T₁w (T₁w_norm) signal as an additional predictor. Measured and predicted fractional enhancement values were compared using the Bland-Altman plot. An optimum threshold for separating necrotic and viable tissue using fractional T₁ enhancement was established using ROC. Results ADC and D (diffusion coefficient) provided the strongest predictors of fractional T₁ enhancement in tumour tissue (p < 0.001). Using the ADC-T₁w_norm model (adjusted R² = 0.4), little bias (mean difference = − 0.093, 95% confidence interval = [− 0.52, 0.34]) was shown between predicted and measured values of fractional enhancement and analysed via the Bland-Altman plot. The optimal threshold for differentiating viable and necrotic tissue was 33% fractional T₁ enhancement (based on measured values, AUC = 0.93; sensitivity = 85%; specificity = 90%). Conclusions Combining ADC and T₁w imaging predicts enhancement in Wilms tumours and reliably identifies and measures necrotic tissue without gadolinium. Key Points • Alternative method to identify necrotic tissue in Wilms tumour without using contrast agents but rather using diffusion and T ₁ weighted MRI. • A method is presented to visualise and quantify necrotic tissue in Wilms tumour without contrast. • The proposed method has the potential to reduce costs and burden to Wilms tumour patients who undergo longitudinal follow-up imaging as contrast agents are not used.